DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li et al. (“Unsupervised deep learning framework for data-driven gating in positron emission tomography” August 2023), hereinafter “Li.”
Regarding claim 1, Li discloses a nuclear medicine diagnosis device (Canon Celstion whole-body TOF PET/CT scanner” P.6050, ¶5 – P.6051, ¶1) comprising:
processing circuitry configured to collect radiation data obtained by detecting radiation based on radioactive medicine administrated to a subject (Canon Celestion whole-body TOF PET/CT scanner comprises processing circuitry to collect list-mode data obtained by detecting radiation based on 216.0-249.9 MBq F-FDG administration to a patient, P.6050, ¶5 – P.6051, ¶1),
divide the radiation data into a plurality of radiation data segments with predetermined time widths (list-mode data is divided into 500 ms frames, P.6050, ¶5 – P.6051, ¶1), and
detect a movement state of a region of interest in a body of the subject based on the radiation data segments (respiratory motion, motion signal, displacement, respiratory phase, and motion field of a ROI in the body of a patient based on the divided list-mode data, P.6050, ¶ 1-2, P.6052, ¶2, P.6052, ¶5 – P.6053, ¶2, P.6058, ¶4).
Regarding claim 2, Li discloses the processing circuitry generates reconstructed images indicating positions of the region of interest based on the radiation data segments (Canon Celestion whole-body TOF PET/CT scanner and computer software implementation using Keras 2.2.4 with Tensorflow backend and NVIDIA GTX 1080TI GPU comprise processing circuitry to generate reconstructed images indicating positions of the region of interest based on the divided list mode data, P.6049, ¶ 4 – P.6050, ¶ 1, P.6050, ¶5 – P.6051, ¶1, P.6052, ¶ 2, P.6052, ¶ 5),
rearranges generated reconstructed images in order of time of respiration of the subject (reconstructed images are sorted in order of respiratory gates, Abstract, P.6048, ¶5, P.6048, ¶6 – P.6049, ¶1, P.6049, ¶4 – P.6050, ¶3, P.6050, ¶5 – P.6051, ¶1, P.6052, ¶5 – P.6053, ¶ 2),
compares the positions of the region of interest between at least two rearranged reconstructed images (respiratory motion, motion signal, displacement, respiratory phase, and motion field of a ROI between at least two of the sorted respiratory gate reconstructed images, P.6050, ¶ 1-2, P.6052, ¶2, P.6052, ¶5 – P.6053, ¶2, P.6058, ¶4), and
detects the movement state of the region of interest based on a comparison result of performing a comparison process (respiratory motion, motion signal, displacement, respiratory phase, and motion field of a ROI determined based on a comparison between at least two of the sorted respiratory gate reconstructed images, P.6050, ¶ 1-2, P.6052, ¶2, P.6052, ¶5 – P.6053, ¶2, P.6058, ¶4).
Regarding claim 3, Li discloses the processing circuitry rearranges the reconstructed images based on feature quantities of the generated reconstructed images (Canon Celestion whole-body TOF PET/CT scanner and NVIDIA GTX 1080TI GPU comprise processing circuitry to sort reconstructed images in order of respiratory gates based on latent features of the generated reconstructed images, Abstract, P.6048, ¶5, P.6048, ¶6 – P.6049, ¶1, P.6049, ¶4 – P.6050, ¶3, P.6050, ¶5 – P.6051, ¶1, P.6052, ¶5 – P.6053, ¶ 2).
Regarding claim 4, Li discloses the processing circuitry extracts the feature quantities including at least movement components in the region of interest indicated in the reconstructed images by inputting the reconstructed images to a learning network and rearranges the reconstructed images based on a degree of similarity between the feature quantities (Canon Celestion whole-body TOF PET/CT scanner and computer software implementation using Keras 2.2.4 with Tensorflow backend and NVIDIA GTX 1080TI GPU comprise processing circuitry to extract the latent features including at least respiratory movement components in the ROI indicated in the reconstructed images by inputting the reconstructed images to a unsupervised feature learning network and sorts the reconstructed images based on similarity between the latent features, Abstract, P.6048, ¶5, P.6048, ¶6 – P.6049, ¶1, P.6049, ¶4 – P.6050, ¶3, P.6050, ¶5 – P.6051, ¶1, P.6052, ¶5 – P.6053, ¶ 2).
Claim 7 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li.
Regarding claim 7, Li discloses a region-of-interest movement state detection method (a method for detecting respiratory motion, Abstract; region of interest movement analysis, P.6052, ¶2) comprising:
collecting, by a computer, radiation data obtained by detecting radiation based on radioactive medicine administrated to a subject (Canon Celestion whole-body TOF PET/CT scanner comprises a workstation computer to collect list-mode data obtained by detecting radiation based on 216.0-249.9 MBq F-FDG administration to a patient, P.6050, ¶5 – P.6051, ¶1);
dividing, by the computer, the radiation data into a plurality of radiation data segments with predetermined time widths (list-mode data is divided into 500 ms frames, P.6050, ¶5 – P.6051, ¶1; Canon Celestion whole-body TOF PET/CT scanner comprises a workstation computer, P.6050, ¶5 – P.6051, ¶1; computer software implementation using Keras 2.2.4 with Tensorflow backend and NVIDIA GTX 1080TI GPU, P.6049, ¶5 – P.6050, ¶1); and
detecting, by the computer, a movement state of a region of interest in a body of the subject based on the radiation data segments (respiratory motion, motion signal, displacement, respiratory phase, and motion field of a ROI in the body of a patient based on the divided list-mode data, P.6050, ¶ 1-2, P.6052, ¶2, P.6052, ¶5 – P.6053, ¶2, P.6058, ¶4; Canon Celestion whole-body TOF PET/CT scanner comprises a workstation computer, P.6050, ¶5 – P.6051, ¶1; computer software implementation using Keras 2.2.4 with Tensorflow backend and NVIDIA GTX 1080TI GPU, P.6049, ¶5 – P.6050, ¶1).
Claim 8 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li.
Regarding claim 8, Li discloses a non-transitory computer-readable storage medium storing a program for causing a computer to (a method implemented on a Canon Celestion whole-body TOF PET/CT scanner comprises a workstation computer and computer software implementation using Keras 2.2.4 with Tensorflow backend and NVIDIA GTX 1080TI GPU, P.6049, ¶5 – P.6050, ¶1, P.6050, ¶5 – P.6051, ¶1):
collect radiation data obtained by detecting radiation based on radioactive medicine administrated to a subject (collect list-mode data obtained by detecting radiation based on 216.0-249.9 MBq F-FDG administration to a patient, P.6050, ¶5 – P.6051, ¶1);
divide the radiation data into a plurality of radiation data segments with predetermined time widths (list-mode data is divided into 500 ms frames, P.6050, ¶5 – P.6051, ¶1); and
detect a movement state of a region of interest in a body of the subject based on the radiation data segments (respiratory motion, motion signal, displacement, respiratory phase, and motion field of a ROI in the body of a patient based on the divided list-mode data, P.6050, ¶ 1-2, P.6052, ¶2, P.6052, ¶5 – P.6053, ¶2, P.6058, ¶4).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Li as in claim 2 above, and further in view of Ren et al. (“Data-driven event-by-event respiratory motion correction using TOF PET list-mode centroid of distribution” 2017), hereinafter “Ren.”
Regarding claim 5, Li discloses the processing circuitry compares positions of the region of interest between a first reconstructed image that is the reconstructed image indicating a state in the respiration of the subject and a second reconstructed image that is the reconstructed image indicating a state in the respiration of the subject (Canon Celestion whole-body TOF PET/CT scanner and computer software implementation using Keras 2.2.4 with Tensorflow backend and NVIDIA GTX 1080TI GPU comprise processing circuitry to calculate the displacement and motion fields of the ROI between a first reconstructed image that is the reconstructed image indicating a state in the respiration of the patient and a second reconstructed image that is the reconstructed image indicating a state in the respiration of the patient, Abstract, P.6050, ¶ 1-2, P.6052, ¶2, P.6052, ¶5 – P.6053, ¶2, P.6058, ¶4).
However, Li does not appear to explicitly disclose the first reconstructed image indicating a maximum expiration state and the second reconstructed image indicating a minimum inspiration state.
However, in the same field of endeavor of PET imaging, Ren teaches comparing positions of the region of interest between a first reconstructed image that is the reconstructed image indicating a maximum expiration state in the respiration of the subject and a second reconstructed image that is the reconstructed image indicating a maximum inspiration state in the respiration of the subject (determining the respiratory displacement of the ROI, pancreas, between end-expiration and end-inspiration from gated reconstructions, Abstract, P.4744, ¶5 – P.4745, ¶1, P.4747, ¶1, Fig. 4, P.4752, ¶ 2).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied Ren’s known technique of calculating the displacement of the ROI between a first end-expiration reconstructed image and a second end-inspiration reconstructed image to Li’s known apparatus for calculating the displacement and motion fields of the ROI between a first and second reconstructed image to achieve the predictable result that this allows for quantitative analysis of the reconstruction results to assess reliability. See, e.g., Ren, P.4744, ¶5 – P.4745, ¶1 and P.4752, ¶2.
Regarding claim 6, Li discloses the processing circuitry obtains a difference between a position of a first region of interest that is the region of interest indicated in the first reconstructed image and a position of a second region of interest that is the region of interest indicated in the second reconstructed image and outputs information indicating the obtained difference as the comparison result (Canon Celestion whole-body TOF PET/CT scanner and computer software implementation using Keras 2.2.4 with Tensorflow backend and NVIDIA GTX 1080TI GPU comprise processing circuitry to calculate the displacement and motion fields of the ROI between a first reconstructed image that is the reconstructed image indicating a state in the respiration of the patient and a second reconstructed image that is the reconstructed image indicating a state in the respiration of the patient, Abstract, P.6050, ¶ 1-2, P.6052, ¶2, P.6052, ¶5 – P.6053, ¶2, P.6058, ¶4).
However, Li does not appear to explicitly disclose the first reconstructed image indicating a maximum expiration state and the second reconstructed image indicating a minimum inspiration state.
However, in the same field of endeavor of PET imaging, Ren teaches comparing positions of the region of interest between a first reconstructed image that is the reconstructed image indicating a maximum expiration state in the respiration of the subject and a second reconstructed image that is the reconstructed image indicating a maximum inspiration state in the respiration of the subject (determining the respiratory displacement of the ROI, pancreas, between end-expiration and end-inspiration from gated reconstructions, Abstract, P.4744, ¶5 – P.4745, ¶1, P.4747, ¶1, Fig. 4, P.4752, ¶ 2).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied Ren’s known technique of calculating the displacement of the ROI between a first end-expiration reconstructed image and a second end-inspiration reconstructed image to Li’s known apparatus for calculating the displacement and motion fields of the ROI between a first and second reconstructed image to achieve the predictable result that this allows for quantitative analysis of the reconstruction results to assess reliability. See, e.g., Ren, P.4744, ¶5 – P.4745, ¶1 and P.4752, ¶2.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al. (“Respiratory signal estimation for cardiac perfusion SPECT using deep learning” August 2023) discloses a deep learning network for generating reconstructed images from divided line mode data, rearranging the data based on respiratory motion, and determining a displacement between the end-expiration and end-inspiration.
Lassen et al. (“Gating approaches in cardiac PET imaging” 2019) discloses a learning network for generating reconstructed images from divided line mode data and rearranging the data based on respiratory motion.
Messerli et al. (“Clinical evaluation of data-driven respiratory gating for PET/CT in an oncological cohort of 149 patients: impact on image quality and patient management” 2021) discloses a learning network for generating reconstructed images from divided line mode data and rearranging the data based on respiratory motion.
Prevrhal et al. (U.S. Pub. No. 2023/0022425) discloses generating reconstructed images from divided line mode data and rearranging the data based on respiratory motion.
Buther et al. (U.S. Pub. No. 2010/0067765) discloses generating reconstructed images from divided line mode data and rearranging the data based on respiratory motion.
Thomas et al. (U.S. Pub. No. 2008/0107229) discloses generating reconstructed images from divided line mode data and rearranging the data based on respiratory motion.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Johnathan Maynard whose telephone number is (571)272-7977. The examiner can normally be reached 10 AM - 6 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Keith Raymond can be reached at 571-270-1790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.M./Examiner, Art Unit 3798
/KEITH M RAYMOND/Supervisory Patent Examiner, Art Unit 3798